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Exploratory pilot study on transcriptome analysis in blood and urine for identifying potential miRNA biomarker candidates to dustinguish of bacterial vs. viral infections in children with fever (blood mRNA)

GSE290693 Homo sapiens (human) RNA-seq (gene expression) 22 samples Submitted 2025/03/28 Platform GPL30209
At a glance

RNA-seq (gene expression) · human · 22 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
22 samples: 22 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Summary (written by the submitters)
There is a lack of fast, reliable and non-invasive methods for distinguishing between bacterial and viral infections in modern clinical practice. It negatively impacts the effectiveness of treatment and increases the risk of unnecessary antibiotic administration and thus antibiotic resistance spread. Using microRNA biomarkers in urine may become the solution to the problems listed.
Read the full summary ↓
miRNAs in urine are relatively stable and can provide an indirect yet specific insight into systemic infections, thus offering a promising diagnostic tool. Combining urine microRNA analysis with blood transcriptomics allows to explore systemic changes that are not limited to a single biological fluid, improving reliability by cross-verifying findings. In this pilot study, the analysis of urine microRNA and blood full transcriptome sequencing data was carried out in children with bacterial (n = 7), viral infections (n = 7) and controls (n = 8) with the goal of determining microRNA diagnostic signatures that distinguish between the said infections, and connecting differentially expressed urine microRNA with differentially expressed transcripts in blood. Using LASSO regularised regression and ANOVA statistical analysis, microRNA biomarker candidates were prioritised and miRNA:mRNA high-likelihood interactions were analysed using correlation analysis and target prediction to further explore systemic changes in response to infection. The differential expression analysis revealed unique features and common patterns in microRNA expression in infections with bacterial or viral etiology. The resultant five microRNAs chosen by both feature selection methods have shown the ability to cluster bacterial infections patients, viral infections and controls. Subsequent correlation analysis and target prediction revealed the role of microRNA regulation in various immune processes such as the interaction of cytokine and cytokine receptors, as well as the regulation of T-cell activation. Our study suggests that prioritised urine microRNA biomarkers have a strong potential for implementation in clinical diagnostics. This could enable a targeted and timely treatment, potentially reducing the risk of spread of antibiotic resistance.
This dataset
Open GSE290693 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE290693_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1229581 and SRA study SRP566839. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42697918) ↗
Study design
22 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 22 sample titles: 22 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
Datasets on a similar topic

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.